A Conceptual Framework for Incorporation of Composting in Closed-Loop Urban Controlled Environment Agriculture
Bibliographic record
Abstract
Controlled environment agriculture (CEA), specifically advanced greenhouses, plant factories, and vertical farms, has a significant role to play in the urban agri-food landscape through provision of fresh and nutritious food for urban populations. With the push towards improving sustainability of these systems, a circular or closed-loop approach for managing resources is desirable. These crop production systems generate biowaste in the form of crop and growing substrate residues, the disposal of which not only impacts the immediate environment, but also represents a loss of valuable resources. Closing the resource loop through composting of crop residues and urban biowaste is presented. Composting allows for the recovery of carbon dioxide and plant nutrients that can be reused as inputs for crop production, while also providing a mechanism for managing and valorizing biowastes. A conceptual framework for integrating carbon dioxide and nutrient recovery through composting in a CEA system is described along with potential environmental benefits over conventional inputs. Challenges involved in the recovery and reuse of each component, as well as possible solutions, are discussed. Supplementary technologies such as biofiltration, bioponics, ozonation, and electrochemical oxidation are presented as means to overcome some operational challenges. Gaps in research are identified and future research directions are proposed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".